# Climate change sentiment on Twitter: An unsolicited public opinion poll

arXiv: https://arxiv.org/abs/1505.03804 · HTML: https://mlsanity.com/paper/1505.03804/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2015.

## Built on by (most cited first, 9 of 9)

- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks](https://mlsanity.com/paper/2303.13373/) (2023)
- [ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries](https://mlsanity.com/paper/2506.13796/) (2025)
- [Short text classification with machine learning in the social sciences: The case of climate change on Twitter](https://mlsanity.com/paper/2310.04452/) (2023)
- [Curating corpora with classifiers: A case study of clean energy sentiment online](https://mlsanity.com/paper/2305.03092/) (2023)
- [Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data](https://mlsanity.com/paper/2312.03095/) (2023)
- [Identifying Climate Targets in National Laws and Policies using Machine Learning](https://mlsanity.com/paper/2404.02822/) (2024)
- [From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media](https://mlsanity.com/paper/2604.21786/) (2026)

## Abstract (arXiv)

The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word "climate" collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.